Predictive Maintenance & Condition Monitoring - A Hot Seat Q&A Session
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Join us for an insightful session as we delve into the intricate world of Predictive Maintenance PDM and Condition Monitoring. Our experts, Gavin Green and Timothy White, engage in a detailed Q&A session, unraveling the complexities and best practices in this field.
hello everybody and welcome to another
webinar from uh from exen Pro um my name
Gavin Green I look after strategic
solutions for exen pro and today I've
got Timothy White uh one of our
engineering Consultants uh with us today
so what are we going to cover into
today's session uh let's just build this
up so condition monitoring predictive
maintenance um essential for optimizing
equipment performance
reducing cost improving safety when you
start bringing in uh digital twins into
that um you can amplify some of these
benefits today's format of our webinar
is going to be a little bit different uh
not just us presenting information uh to
to the users um and demos Etc what we're
actually going to do is we're going to
do going to go through a few discussion
points specifically around condition
monitoring predictive maintenance uh
from an engineer's perspective um so
let's Dive Right In to that Tim do you
want to just give us a quick intro to
yourself please sure thanks Kevin uh
yeah I um I've done three years as a
reliability engineer in both process and
open pit and now I work as a software
consultant engineering consultant for XM
Pro perfect and also uh heavily involved
and and leading different projects so
Weir nutrient or a few of them as well
um and very active and he keeps pressing
us around process Improvement Etc so uh
very very very big advocate for that
Insight some of the topics that we're
going to cover um so fundamentals of
condition monitoring predictive uh we
have to start with a baseline where are
we coming from and then move from there
we're then going to go into Data
integration uh and
simulation um bringing in some realtime
data and iot integration we then going
to touch on some imp mation challenges
uh some of the data analytics and then
some best practices in predictive
maintenance um as well before we go into
these topic areas though um it's always
good to to just make sure that we have
some uh alignment on ter terminology so
we're all talking the same and we're not
talking past each other so just some of
the the common terms so when we're
talking around condition monitoring
we're talking around the the process to
continuously Monitor and assess perform
perance and health of Machinery um to
detect issues and prevent unplanned
downtime when we're talking about
predictive maintenance uh we're talking
about a proactive approach using data
analytics machine learning to predict
equipment failures so that we can
perform timely maintenance uh of them as
well and then the third prescriptive
maintenance um again using data
analytics machine learning this one
recommends specific actions and optimal
timing for maintenance tasks um to to
maximize reliability and minimize
downtime the terminology around a
digital twin So exm Pro is part of the
digital twin Consortium um the
definition uh from the Consortium does
not include the modelbased uh item that
we've got there um however we feel that
is uh it is part of the definition so
when we're talking digital twins we're
talking around a model-based virtual
representation of World entities and
processes synchronized at a specific
frequency and Fidelity we're going to
talk around digital twins as we go
through and and how that links into
condition monitoring predictive Etc but
just to make sure we're all on the same
Baseline what is it that we're talking
around and um the uh the common
definitions to to build from um as
well so let us move straight in
um to the first topic so so we're going
to put you in the hot seat uh hot seat
here Tim so to speak so the first area
that we're going to chat around is the
fundamentals of condition monitoring and
predictive maintenance so I've got a few
questions on the side here um we're not
going to bring the questions up be happy
to share with them after the webinar as
well so the first one companies looking
to to implement uh predictive
maintenance solution they often tell me
they're not ready for it um they don't
know where to start this is probably not
a fit for them how do you approach these
types of discussions with them that's uh
a really good question you have to
convince people also to spend money and
open the pur strings um so in in
reliability there's uh there's a
maintenance hierarchy that's typically
based on program maturity um we start
out at reactive preventative condition
based and predictive and prescriptive
you kind of touched on it a little bit
earlier uh I would say that company that
tries to do everything all at once is
going to waste quite a bit of money and
ruin any Goodwill that the reliability
group has generated with maintenance and
operations uh nobody starts with
prescriptive maintenance without quite a
bit of pre-work they start small with
condition-based monitoring and then move
into a PDM program um following that
maintenance hierarchy I would advise
this company to start with CD CBM or
condition-based monitoring and then move
to prescriptive and predictive modeling
uh as applicable based on asset
criticality okay okay now I do like that
um the fact that there is a hierarchy
there um and there there's there's steps
to to get to it so people don't just
jump into predictor maintenance from the
GetGo with nothing there's a few puddles
as we call them to to go through to to
actually get them um turning a little
bit though um how does condition
monitoring and predictive maintenance
benefit from bringing in and integrating
digital Twins and event intelligent
platforms into
that um that is a good question I I'll
start out with what uh an event
intelligence platform is um an event
intelligence
platform uh helps analyze data collected
from a variety of sources including
digital twins uh they help identify
patterns Trends uh anomal
IES um they can significantly help PDM
and condition monitoring reliability
groups by giving uh near real-time
alerts um to operation
staff uh using machine learning models
to detect deviations of sensors and
processes um they call it decision
support uh prescriptive
analytics um what that means is when a
certain predefined event criteria is
generated a uh alert or warning with
specific instructions on how to fix the
issue or mitigate the issue is also
generated in response to that uh they're
also incredibly useful for historical
data analysis
um I guess moving on to digital twins uh
they ideally ref reflect the current
condition of the physical
counterart uh this integration allows
for reliability teams to uh give
maintenance groups the ability to
visualize the uh they call it the life
cycle of a system and a controlled
environment um meaning from install to
failure they can model it with enough
data um reliability Engineers on that
note uh they can predict failures using
real-time data uh there's there's a
concept in reliability called the
potential for failure curve um based on
St statistical modeling you can put a
particular asset uh at a likelihood of
failure um planners can optimize
maintenance schedules basing rotation
off of condition rather than just time
and then uh on the more mature side data
scientists can test uh input scenarios
and improve designs through machine
learning
simulations um integrating digital Twins
and inet intelligence the I the most
important part is the the learning curve
is significantly shortened um you you
codify the uh experienced operator
intuition and
knowledge okay quite a bit to unpack
there um if we move into Data
integration and and
simulation so we've we've gone
through how do we get to condition
monitoring or predictive or prescriptive
of um the role the digital twin can
bring into that why it's
beneficial for all this to work
though data integration and simulation
going to play a key piece so what role
does data integration and
simulation play in condition monitoring
and predictive
maintenance uh a big one
so yeah uh data integration and
simulations play I call them crucial
roles in predictive maintenance and
condition monitoring uh especially
combined with digital Twins and event
intelligence platforms it's it's the
building
block uh focusing on data integration
first um just to make sure everyone's on
the same page it refers to the process
of combining data from multiple sources
into a single unified view um
consolidation of historical and
real-time data means merging historical
data with operational data patterns and
Trends can be more accurately
identified um it's it's your work order
history it's your shift notes it's your
vibration data in one
display um ideally with uh with regards
to simulation for condition monitoring
it uses computational models and machine
learning and and the more advanced
programs they they de to Ai and tensor
flow and things like that but it the
with the goal the main goal is to
imitate operations of real or processes
and systems um simulation allows for
testing the predictive model and uh
ensuring they're accurate before
applying them to actual equipment and
operations um lowrisk testing for V uh
variables scenario planning also helps
understanding how different conditions
might affect the system um game theing
better preparation and response
strategies uh lastly simulation provides
uh a virtual environment for training
operators and
technicians uh you you remove the risk
of actually damaging equipment and also
giving the operator an intuition without
that
risk together I guess data integration
and simulation they support creation of
a holistic
all-encompassing proactive maintenance
strategy um better decision- making they
provide detailed insights into equipment
Health under various conditions um
thereby I guess the the end result is
reducing downtime and extending life of
uh the
asset so is it also safe to say that uh
um from a data integration perspective
by combining all these different data
sources together um it allows for more
complex analysis which helps the
accuracy of these predicted results so
these two tend to work hand in hand yeah
yeah absolutely it's it's the building
block perfect perfect okay um so with
that being said though and the
criticality of the two of these and and
how they all work together um what
challenges exist in integrating some of
these data
sources uh quite a few um I guess one of
the biggest ones is uh call them data
silos so um different departments
different
organizations uh they have their own
systems and ways of doing things that uh
don't communicate very well with each
other and they can hinder that uh
unified view of data that a digital twin
requires um for example uh truck liner
bed thicknesses are on an Excel document
on one computer and fuel records are on
an Excel document and another computer
um both are useful to digital twins but
they aren't useful in their card format
and need to be exposed to the digital
twin um data quality and consistency is
another big one uh ensuring data is
accurate and up to dat and consistent
across the various sources is crucial um
because you're relying on that for
decision support poor data quality can
lead to incorrect analysis and faulty
predictions um complexity of integration
the data silos kind of touched on it but
um multiple data sources in Legacy
systems typically don't talk very well
with each
other um real-time data processing it's
it's costly it's expensive in in time
and money uh the ability to process
realtime data is challenging um but it's
also essential for timely decision
making for your PM groups operations
groups um scalability is another big one
uh the system must be able to scale with
growth and data and simulations without
also growing in latency nobody wants to
work on a slow system
um models in simulation
require
uh computational resources and the the
better the model uh or the excuse me the
I guess um models require High Fidelity
so the the better models require more
data and that's challenging without
continuous refinement and validation
outcomes and I touched on computational
resources the the more simulations you
run
um the more computationally intensive it
gets um cyber security is a big one as
we've all seen uh you're putting all of
your data in one bread basket and making
it a much bigger Target so cyber
security for everyone working is
incredibly
important um expertise in in digital
Twins and in reliability you you have to
be able to speak across departments for
both both the data scientists and the uh
reliability
folks um and we spoke on cost change
management is also a big one uh
workforces are generally resistant
anytime you have a technology like
digital Twins or event intelligence
machine learning um how the how the
software is rolled out and who's
involved and who has a stake in it can
make or break the program
um and then Regulatory Compliance as
well uh ensuring data handling and
processing comply with relevant
regulations whether it be socks or some
other regulation that I'm not aware of
um especially in industries that are
heavily
regulated uh addressing challenges
addressing the challenges requires
combination of strategic planning and
investment in technology and training
nobody's just going to happen upon it
and then development of new processes
and government or governance models for
data okay so there quite a few there's
quite a few there to unpack um if we if
we just focus on the the realtime um
aspect of this so real-time data because
you you alluded to it earlier around um
passing real-time data to the models uh
Etc so in in your experience what are
the biggest challenges and
opportunities when inter integrating iot
devices with intelligent digital twins
um specifically for realtime monitoring
in predictive maintenance condition
monitoring
Etc uh integrating iot devices presents
quite a few challenges into the um
digital twin uh one of the biggest ones
is data volume and management every iot
sensor you have um depending on the
uptake um generates quite a bit of data
and managing it can be it it needs
expertise if it everybody's dealt with a
system that isn't designed well um we've
talked about complex uh complexity of
integration but it's important so I'm
gonna repeat it uh it's iot systems
generally don't talk to each other so um
you need somebody that can go back and
forth between uh sensor compatibility
quality and connectivity is huge uh that
an accurate and expensive iot sensor
doesn't function without a reliable and
secure wireless network and guess the
other side of the coin
is uh there's no point to having a
reliable and secure network if the data
you're sending over it is inconsistent
and not
reliable interoperability of sensor
systems is big does your vibration
monitor also allow you to integrate uh
temperature
sensor
um regarding the change management side
of it and hesitancy to adopt new
technologies kind of along the same vein
nobody wants to go to five different
websites to interact with five different
sensor
types um I'm sure everybody's been in
that situation too uh lat latency and
skill of ility we we we talked about
latency a little bit but uh we need
asset views to load quickly regardless
of how many sensors or similar systems
exist and then maintenance um
maintenance of the digital twin is
crucial uh depending on size and
complexity they normally require subject
matter experts to maintain and modify
the uh hmis or human machine interfaces
but just like any other system you have
to maintain it uh that being said the
the rewards for a good integration are I
like to think they're exponential um the
pre-work done for one required for One
sensor is the same pre-work design for
20
sensors um data silos are somewhat
mitigated using
pipelines um exposing data making it
more easily
digestible uh it improves asset
performance through that proactive
maintenance strategy we talked about
prescriptive maintenance and then uh
economies of scale um the fixed cost for
sensor implementation becomes much more
palatable the more sensors you have
attack so that fixed cost per goes
down um and then transparency is Big so
all the stakeholders whether it be it or
reliability or maintenance or operations
are all working off the a common set of
data and then a short and learning curve
so the uh optimal State Intuition or
plant intuition the 20-year plant
operator has um can be codified into
that digital twin and that's why it's so
important to have that buy in from these
operators and mechanics that have been
it since I was in college um but yeah uh
another reward is automated decision
making um with the right integration
systems can automatically adjust
processes uh in response to data from
iot devices without hum uh human
intervention and then um safety
it it's a iot censor can exist in an
environment where a person can't U so
there absolutely
necessary um operational efficiencies
are improved when uh equipment is
operating at optimal levels everything
likes to run in steady state and that's
aided by iot
um addressing the challenges involves uh
crossplatform mix of technological
solutions strategic planning and ongoing
management it's not fire and forget
um yeah and then opportunities but
interesting um can you share a success
story uh where iot
integration um with a digital twin has
significantly
improved um either condition monitoring
or predictive maintenance
outcome uh yeah it and this also goes to
show how easily this stuff can be
integrated into your system um so a
while back
when uh I was working in
reliability we had a cone crusher that
would get packed in during Implement
weather uh the cone would bounce um once
that dirt gets packed in it becomes
basically cement and as the crusher
spins around it hops and if you can
imagine uh a 50 ton block of iron
hopping the uh wear and tear on it was
pretty tremendous it would sh shake the
entire building frame so obviously that
was not a desired outcome um the
vibration monitors on the cone crusher
were internal to it um so they could be
changed unless the cone crusher was shut
down so a operator came to me with the
idea to um use a Raspberry Pi and a
microphone
to
uh create a decel meter that wrote to
our
historian um so with a little bit of
python code and a Wi-Fi signal we we
created a proof of concept sensor that
uh fed directly into the control room
HMI and it worked in um backing up the
vibration monitors and sometimes um
replacing them whenever they went down
so um yeah worked out well and it was
cheap
uh cheap cheap uh in the grander scheme
of things yeah cheap in at
work okay so you've touched on some of
uh some of the items there um focusing
on implementation and and data um
analytics um what are some of the
implementation challeng implementation
challenges um of a predictive uh
maintenance solution when involving
digital
terms uh sure the the implement
challenges um for a digital twin for PDM
um they're call it multifaceted uh they
involve Technical Resources from it they
involve advice from reliability groups
operational resources and like I was
talking about those 20-year mechanics
and operators to provide advice in that
Common Sense
check um integrating with existing
systems we we sort of touched on on the
data stream designer earlier but this is
where it proves its worth uh for that
cone crusher example I was able to
perform the task without a lot of
pre-work because uh someone had built a
python library to access our historian
and
rewrite um without that library and
connection to the API it would have been
pretty much impossible at my current
skill level um the data stream designer
has quite a few options for interacting
with uh historians apis databases um
makes the barrier to entry much lower
when the API already
written um another uh challenge is data
collection and quality uh your model is
only as good as the sensors and the
people who help create it um some assets
need sophisticated sensors and some
don't take for instance a a redundant
pump you don't need to throw prescripted
maintenance and machine learning models
and everything like that if it's a $100
replacement and it's got a
redundancy um so you have to weigh asset
criticality as well
as ability and then uh it's this is a
this is a big one it delves into change
management a little bit it's absolutely
necessary that your IT personnel and
your data scientists at least have uh a
rudimentary understanding of the goals
behind a PDM digital twin and uh the
other side of that coin is your PDM
group has to be able to communicate
their needs to the data scientist team
the IT
team um model development validation and
maintenance is also another big
challenge um like I said before digital
twins aren't fire and
forget uh in addition to the regular
maintenance required by any software you
also have to consider business needs of
the plant change over time and the
digital twin has to evolve and grow uh
with the
operation oh go ahead you you touched on
it earlier and you touched on a little
bit here as well um so data analytics
and machine
learning how would they enhance some of
the predictive
capabilities uh data analytics and
machine learning for the more mature
programs um they are designed inherently
to detect patterns in data
sets um whether through supervised
learning or unsupervised
learning pattern recognition is what
they were designed for finding hidden
correlations um they also help in
anomaly of abnormal operating
parameters um predictive
maintenance uh excuse me predicted
modeling um what happens if I change X
and How likely is it going to be to
happen
um for optimizing maintenance schedules
and life cycle management um for example
you take a truck engine or a hall truck
engine uh life cycle can be 20,000 hours
or 40,000 hours depending on condition
and when you're
talking um a million and a half $2
million for an engine replacement it can
definitely affect the bottom line of a
company changing on condition rather
than
time root cause analysis is another one
um aided by anomaly detection and then
the findings of the root cause can then
be fed back into the digital twin to uh
enhance the prescriptive aspect of it
okay and enance decision making and
resource allocation
um clear analysis of data helps support
decision making and prioritizing
maintenance
activities perfect moving on to our last
uh section in our uh our last question
here just keeping an our time for the
folks as well is can you highlight some
best practices um in condition
monitoring and predictive
maintenance sure um some of the best
practices involve you you have to have a
strategy to start off with um clear
goals clear
objectives uh also you have to select
the appropriate
assets not every asset needs
prescriptive analytics like I was
talking about um integrate data
sources operational maintenance records
sensor data and environmental data were
available uh invest in quality
sensors use data analytics and machine
learning where applicable to helped prct
failure um ensure real-time
monitoring regularly update the machine
learning models to reflect current state
uh train staff create a feedback
loop um and most importantly communicate
effectively across departments for um
visibility
sake okay you you touched on a few
interesting ones there um so from an XM
Pro perspective um we follow some of
those best practices ourselves so how do
we how do we actually do that um the
first You' mentioned so identifying and
prioritizing Bad actors two predicting
in real time using a hybrid approach so
how do we bring in some of the you've
touched on condition monitoring
predictive prescriptive so how from a
hybrid approach can you bring some of
those pieces in there as well and then
the last piece is uh a quick time to
Value um for this um and that's aided
with our blueprints templates um Etc as
as
well I'd like to thank you uh thank you
Tim thank you for the time running
through this being being sitting in the
hot seat there so to speak U answering
the questions and things uh put to you
thank you all for uh listening and
attending as well um our our next
webinar next month um again there's two
options for you to attend uh from timing
perspective uh what we're going to do is
go through uh root cause analysis
application so we actually have a
blueprint for that as well how you
capture the recommendations value impact
Etc and as always if you've got any
questions feedback um we just want to
contact us for more information right at
the bottom there just send us an email
and we'll be happy to uh uh to provide
whatever it is uh you're looking for so
again Tim thank you for your time uh and
everyone else thank you for uh attending
today
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